The present invention relates to a recommender system, a recommendation method, and a program.
According to an explosive quantitative increase and qualitative diversification of resources and information (items) available through a network, it is becoming difficult for individual users to access items that the users want when the users want the items. Therefore, there is a demand for a recommender system and a platform that provide optimum items taking into account context information that changes every moment such as characteristics, preference information, and situations of individuals. For this purpose, a context-aware recommender system customized for individual users becomes necessary that handles static information of which temporal change is slow such as preference information of the users and dynamic information that changes every moment such as the present locations.
The recommender system needs to provide items that the users are highly likely to evaluate as high quality. In the conventional recommender system, various methods have been proposed to improve satisfaction levels of provided information. As a simplest and general method, there is a method of calculating an average of evaluation results (ratings) of items referring to experiences in the past of other users. There is high probability that many users give high evaluation to high quality items. Therefore, in particular, when there are a large number of users, a certain effect can be expected.
For example, Patent Document 1 describes a system that extracts similarity among items from a history of items purchased by users in the past and displays items highly evaluated out of items having high similarity to items already known to attract interests of the users. However, if items highly evaluated in average are simply displayed, uniform information is provided to all the users. Therefore, the system sometimes does not work when there are only a small number of users or when only a small number of users have the same preference.
Therefore, there is known a method of analyzing preference characteristics of users from histories or the like of the users and calculating a weighted average with weight placed on opinions of users having high similarity to target users (active users) to be recommended. This is a method called Collaborative Filtering. The method is based on the premise that the active users feel, at higher probability, that opinions of users having senses of values similar to the senses of values of the active users are high quality. In considering the similarity, it is necessary to consider preference tendencies and present situations (contexts) that influence the interests of the users.
As the contexts, there are a static context and a dynamic context. As an example of the static context of which temporal change is relatively slow, there is preference information of users concerning items such as books, movies, and news. In an online shopping site, a word-of-mouth information site, or the like, commodities and items to be recommended are determined by estimating preference information of active users. Typically performed processing is processing for specifying users having highly similar behavior histories from behavior histories such as purchase histories of users and displaying items highly evaluated by the users. This is based on the premise that users having similar purchase histories are considered to have similar preferences and items highly evaluated by one user are highly likely to be highly evaluated by the other user. For example, Non-Patent Document 1 describes a system that recommends news on the basis of preference information of users having preference tendencies similar to the preference tendencies of active users.
Examples of a recommender system that uses the dynamic context include a real space application. The present location is a representative example of a user context that changes every moment. In the real space application, the present locations of users are acquired and managed using a GPS or the like and items related to the present locations are recommended. Similarity here is closeness of distances in terms of geodetic coordinates. The real space application is applied to, for example, a social networking application that displays other users present near active users and a recommender system that displays popular restaurants, sightseeing spots, and the like in neighborhoods.
On the other hand, users having high similarity do not always have a skill for performing correct value judgment. Therefore, there is a method of systematically selecting an authority user by evaluating evaluations themselves of users. This method is applied to, for example, extraction of an adviser highly evaluated in a Q&A site in which a plurality of users answer a question of a questioner. Specifically, other users evaluate a comment of a user and select advice of an authority user highly evaluated by the other users, whereby the method is realized.
Similarity among users and information concerning authority degrees are combined and items recommended by users having high similarity and high authority degrees are preferentially selected. This makes it possible to perform highly accurate recommendation.
Patent Document 2 discloses an information providing system including an information registering section configured to register respective kinds of browsing information while associating the browsing information with classification tags representing to which classifications contents of the browsing information correspond, an evaluation acquiring section configured to acquire evaluations by users who have already browed the respective kinds of browsing information, a user-information accumulating section configured to accumulate, together with identification information of the users, evaluations concerning the respective kinds of browsing information acquired by the evaluation acquiring section, a user-classification determining section configured to determine, from a relative relation of evaluations among a plurality of users accumulated in the user-information accumulating sections, a relation among the users for each of the classification tags associated with the respective kinds of browsing information, an information-classification determining section configured to determine, among a plurality of classification tags different from one another, a relation among the plurality of classification tags from a difference in a relation among the users determined by the user-classification determining section and a difference in evaluations for the users with respect to the browsing information affixed with the respective classification tags, and an information output section configured to select, when a specific user requests, concerning any one of the kinds of browsing information, browsing performed by using a keyword, the browsing information suitable for the specific user on the basis of a relation between the specific user and the other users determine by the user-classification determining section concerning a classification tag different from a specific classification tag to which the keyword belongs and a relation between the specific classification tag and the different classification tag determined by the information-classification determining section.
Patent Document 1: U.S. Pat. No. 6,266,649 Gregory D. Linden, Jennifer A. Jacobi, Eric A. Benson, Collaborative recommendations using item-to-item similarity mappings
Patent Document 2: Patent Publication JP-A-2009-181428
Non-Patent Document 1: Abhinandan S. Das, Mayur Datar, Ashutosh Garg, Shyam Rajaram, “Google news personalization: scalable online collaborative filtering”, In WWW '07: Proceedings of the 16th international conference on World Wide Web (2007), pp. 271-280
Non-Patent Document 2: J. Kleinberg, “Authoritative sources in a hyperlinked environment”, In Proceedings of the Ninth Annual ACM-SIAM, Symposium on Discrete Algorithms, 1998, pp. 668-677